4,123 research outputs found
CDR: Conservative Doubly Robust Learning for Debiased Recommendation
In recommendation systems (RS), user behavior data is observational rather
than experimental, resulting in widespread bias in the data. Consequently,
tackling bias has emerged as a major challenge in the field of recommendation
systems. Recently, Doubly Robust Learning (DR) has gained significant attention
due to its remarkable performance and robust properties. However, our
experimental findings indicate that existing DR methods are severely impacted
by the presence of so-called Poisonous Imputation, where the imputation
significantly deviates from the truth and becomes counterproductive.
To address this issue, this work proposes Conservative Doubly Robust strategy
(CDR) which filters imputations by scrutinizing their mean and variance.
Theoretical analyses show that CDR offers reduced variance and improved tail
bounds.In addition, our experimental investigations illustrate that CDR
significantly enhances performance and can indeed reduce the frequency of
poisonous imputation
Asymmetric Tri-training for Debiasing Missing-Not-At-Random Explicit Feedback
In most real-world recommender systems, the observed rating data are subject
to selection bias, and the data are thus missing-not-at-random. Developing a
method to facilitate the learning of a recommender with biased feedback is one
of the most challenging problems, as it is widely known that naive approaches
under selection bias often lead to suboptimal results. A well-established
solution for the problem is using propensity scoring techniques. The propensity
score is the probability of each data being observed, and unbiased performance
estimation is possible by weighting each data by the inverse of its propensity.
However, the performance of the propensity-based unbiased estimation approach
is often affected by choice of the propensity estimation model or the high
variance problem. To overcome these limitations, we propose a model-agnostic
meta-learning method inspired by the asymmetric tri-training framework for
unsupervised domain adaptation. The proposed method utilizes two predictors to
generate data with reliable pseudo-ratings and another predictor to make the
final predictions. In a theoretical analysis, a propensity-independent upper
bound of the true performance metric is derived, and it is demonstrated that
the proposed method can minimize this bound. We conduct comprehensive
experiments using public real-world datasets. The results suggest that the
previous propensity-based methods are largely affected by the choice of
propensity models and the variance problem caused by the inverse propensity
weighting. Moreover, we show that the proposed meta-learning method is robust
to these issues and can facilitate in developing effective recommendations from
biased explicit feedback.Comment: 43rd International ACM SIGIR Conference on Research and Development
in Information Retrieval (SIGIR '20
Causal Inference in Recommender Systems: A Survey and Future Directions
Recommender systems have become crucial in information filtering nowadays.
Existing recommender systems extract user preferences based on the correlation
in data, such as behavioral correlation in collaborative filtering,
feature-feature, or feature-behavior correlation in click-through rate
prediction. However, unfortunately, the real world is driven by causality, not
just correlation, and correlation does not imply causation. For instance,
recommender systems might recommend a battery charger to a user after buying a
phone, where the latter can serve as the cause of the former; such a causal
relation cannot be reversed. Recently, to address this, researchers in
recommender systems have begun utilizing causal inference to extract causality,
thereby enhancing the recommender system. In this survey, we offer a
comprehensive review of the literature on causal inference-based
recommendation. Initially, we introduce the fundamental concepts of both
recommender system and causal inference as the foundation for subsequent
content. We then highlight the typical issues faced by non-causality
recommender system. Following that, we thoroughly review the existing work on
causal inference-based recommender systems, based on a taxonomy of three-aspect
challenges that causal inference can address. Finally, we discuss the open
problems in this critical research area and suggest important potential future
works.Comment: Accepted by ACM Transactions on Information Systems (TOIS
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